Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment Y Liu, R Bucknall Ocean engineering 97, 126-144, 2015 | 256 | 2015 |
Smoothed A* algorithm for practical unmanned surface vehicle path planning R Song, Y Liu, R Bucknall Applied Ocean Research 83, 9-20, 2019 | 225 | 2019 |
A survey of formation control and motion planning of multiple unmanned vehicles Y Liu, R Bucknall Robotica 36 (7), 1019-1047, 2018 | 169 | 2018 |
A multi-layered fast marching method for unmanned surface vehicle path planning in a time-variant maritime environment R Song, Y Liu, R Bucknall Ocean Engineering 129, 301-317, 2017 | 145 | 2017 |
Learn to navigate: cooperative path planning for unmanned surface vehicles using deep reinforcement learning X Zhou, P Wu, H Zhang, W Guo, Y Liu Ieee Access 7, 165262-165278, 2019 | 118 | 2019 |
The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method Y Liu, R Bucknall Applied Ocean Research 59, 327-344, 2016 | 96 | 2016 |
Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations Y Liu, R Bucknall Neurocomputing 275, 1550-1566, 2018 | 95 | 2018 |
Decision-making for the autonomous navigation of maritime autonomous surface ships based on scene division and deep reinforcement learning X Zhang, C Wang, Y Liu, X Chen Sensors 19 (18), 4055, 2019 | 92 | 2019 |
A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network X Chen, Y Liu, K Achuthan, X Zhang Ocean Engineering 218, 108182, 2020 | 80 | 2020 |
The fast marching method based intelligent navigation of an unmanned surface vehicle Y Liu, R Bucknall, X Zhang Ocean Engineering 142, 363-376, 2017 | 77 | 2017 |
Intelligent multi-task allocation and planning for multiple unmanned surface vehicles (USVs) using self-organising maps and fast marching method Y Liu, R Song, R Bucknall, X Zhang Information Sciences 496, 180-197, 2019 | 73 | 2019 |
A robust localization method for unmanned surface vehicle (USV) navigation using fuzzy adaptive Kalman filtering W Liu, Y Liu, R Bucknall IEEE Access 7, 46071-46083, 2019 | 65 | 2019 |
Predictive navigation of unmanned surface vehicles in a dynamic maritime environment when using the fast marching method Y Liu, W Liu, R Song, R Bucknall International Journal of Adaptive Control and Signal Processing 31 (4), 464-488, 2017 | 51 | 2017 |
Optimised MOPSO with the grey relationship analysis for the multi-criteria objective energy dispatch of a novel SOFC-solar hybrid CCHP residential system in the UK X Yuan, Y Liu, R Bucknall Energy Conversion and Management 243, 114406, 2021 | 44 | 2021 |
Smartphone-app based point-of-care testing for myocardial infarction biomarker cTnI using an autonomous capillary microfluidic chip with self-aligned on-chip focusing (SOF) lenses C Liang, Y Liu, A Niu, C Liu, J Li, D Ning Lab on a Chip 19 (10), 1797-1807, 2019 | 42 | 2019 |
Uninterrupted path planning system for Multi-USV sampling mission in a cluttered ocean environment S MahmoudZadeh, A Abbasi, A Yazdani, H Wang, Y Liu Ocean engineering 254, 111328, 2022 | 41 | 2022 |
Two-phase energy efficiency optimisation for ships using parallel hybrid electric propulsion system Y He, A Fan, Z Wang, Y Liu, W Mao Ocean engineering 238, 109733, 2021 | 41 | 2021 |
WODIS: Water obstacle detection network based on image segmentation for autonomous surface vehicles in maritime environments X Chen, Y Liu, K Achuthan IEEE Transactions on Instrumentation and Measurement 70, 1-13, 2021 | 40 | 2021 |
Unsupervised learning based coordinated multi-task allocation for unmanned surface vehicles S Ma, W Guo, R Song, Y Liu Neurocomputing 420, 227-245, 2021 | 38 | 2021 |
Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning P Wu, J Partridge, E Anderlini, Y Liu, R Bucknall International Journal of Hydrogen Energy 46 (80), 40022-40040, 2021 | 37 | 2021 |